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Congenital Nystagmus (CN) is an ocular-motor disorder characterised by involuntary, conjugated ocular oscillations, and its pathogenesis is still unknown. The pathology is defined as “congenital” from the onset time of its arise which could be at birth or in the first months of life. Visual acuity in CN subjects is often diminished due to nystagmus continuous oscillations, mainly on the horizontal plane, which disturb image fixation on the retina. However, during short periods in which eye velocity slows down while the target image is placed onto the fovea (called foveation intervals) the image of a given target can still be stable, allowing a subject to reach a higher visual acuity. In CN subjects, visual acuity is usually assessed both using typical measurement techniques (e.g. Landolt C test) and with eye movement recording in different gaze positions. The offline study of eye movement recordings allows physicians to analyse nystagmus main features such as waveform shape, amplitude and frequency and to compute estimated visual acuity predictors. This analytical functions estimates the best corrected visual acuity using foveation time and foveation position variability, hence a reliable estimation of this two parameters is a fundamental factor in assessing visual acuity. This work aims to enhance the foveation time estimation in CN eye movement recording, computing a second order approximation of the slow phase components of nystag-mus oscillations. About 19 infrared-oculographic eye-movement recordings from 10 CN subjects were acquired and the visual acuity assessed with an acuity predictor was compared to the one measured in primary position. Results suggest that visual acuity measurements based on foveation time estimation obtained from interpolated data are closer to value obtained during Landolt C tests.

The aim of the study is to characterize the local muscles motion in individuals undergoing whole body mechanical stimulation. In this study we aim also to evaluate how subject positioning modifies vibration dumping, altering local mechanical stimulus. Vibrations were delivered to subjects by the use of a vibrating platform, while stimulation frequency was increased linearly from 15 to 60 Hz. Two different subject postures were here analysed. Platform and muscles motion were monitored using tiny MEMS accelerometers; a contra lateral analysis was also presented. Muscle motion analysis revealed typical displacement trajectories: motion components were found not to be purely sinusoidal neither in phase to each other. Results also revealed a mechanical resonant-like behaviour at some muscles, similar to a second-order system response. Resonance frequencies and dumping factors depended on subject and his positioning. Proper mechanical stimulation can maximize muscle spindle solicitation, which may produce a more effective muscle activation.

Congenital nystagmus is an ocular-motor disorder characterised by involuntary, conjugated and bilateral to and fro ocular oscillations. In this study a method to recognise automatically jerk waveform inside a congenital nystagmus recording and to compute foveation time and foveation position variability is presented. The recordings were performed with subjects looking at visual targets, presented in nine eye gaze positions; data were segmented into blocks corresponding to each gaze position. The nystagmus cycles were identified searching for local minima and maxima (SpEp sequence) in intervals centred on each slope change of the eye position signal (position criterion). The SpEp sequence was then refined using an adaptive threshold applied to the eye velocity signal; the outcome is a robust detection of each slow phase start point, fundamental to accurately compute some nystagmus parameters. A total of 1206 slow phases was used to compute the specificity in waveform recognition applying only the position criterion or adding the adaptive threshold; results showed an increase in negative predictive value of 25.1% using both features. The duration of each foveation window was measured on raw data or using an interpolating function of the congenital nystagmus slow phases; foveation time estimation less sensitive to noise was obtained in the second case.

Whole body vibration (WBV) aims to mechanically activate muscles by eliciting stretch reflexes. Mechanical vibrations are usually transmitted to the patient body standing on a oscillating plate. WBV is now more and more utilized not only for fitness but also in physical therapy, rehabilitation and in sport medicine. Effects depend on intensity, direction and frequency of vibration; however, the training frequency is one of the most important factors involved. A preliminary vibratory session can be dedicated to find the best vibration frequency for each subject by varying, stepwise, the stimulation frequency and analyzing the resulting EMG activity. This study concentrates on the analysis of muscle motion in response to a vibration frequency sweep, while subjects held two different postures. The frequency of a vibrating platform was increased linearly from 10 to 60 Hz in 26 s, while platform and single muscles (Rectus Femoris, Biceps Femoris – long head and Gastrocnemius Lateralis) motions were monitored using tiny, lightweight three-axial MEMS accelerometers. Displacements were estimated integrating twice the acceleration data after gravity contribution removal. Mechanical frequency response (amplitude and phase) of the mechanical chains ending at the single muscles was characterized. Results revealed a mechanical resonant-like behavior at some muscles, very similar to a second-order system in the frequency interval explored; resonance frequencies and dumping factors depended on subject and its positioning onto the vibrating platform. Stimulation at the resonant frequency maximizes muscle lengthening, and in turn muscle spindle solicitation, which produce muscle activation.

Cardiotocographic data provide physicians information about foetal development and, through assessment of specific parameters (like accelerations, uterine contractions, ...), permit to assess conditions such as foetal distress. An incorrect evaluation of foetal status can be of course very dangerous. In the last decades, to improve interpretation of cardiotocographic recordings, great interest has been dedicated to FHRV spectral analysis. It is worth reminding that FHR is intrinsically an uneven series and that to obtain evenly sampled series, many commercial cardiotocographs use a zero-order interpolation (storage rate of CTG data equal to 4 Hz). This is not suitable for frequency analyses because interpolation introduces alterations in the FHR power spectrum. In particular, this interpolation process can produce artifacts and an attenuation of the high-frequency components of the PSD that, for example, affects the estimation of the sympatho-vagal balance (SVB - computed as low-frequency/high-frequency ratio), which represents an important clinical parameter. In order to estimate the frequency spectrum alterations due to zero-order interpolation and other CTG storage rates, in this work, we simulated uneven FHR series with set characteristics, their evenly spaced versions (with different storage rates) and computed SVB values by PSD. For PSD estimation, we chose the Lomb method, as suggested by other authors in application to uneven HR series

A valuable alternative to US cardiotocography, for fetal surveillance, can be offered by phonocardiography, a passive and low cost acoustic recording of fetal heart sounds. A crucial point is the exact recognizing of the fetal heart sounds, associated to each fetal heart beat, and then the estimation of FHR signal. In this work, software for FHR assessment from phonocardiographic signals was developed. To check the reliability of the software, obtained results were compared with those of simultaneously recorded cardiotocographic signals. Results seemed to be satisfying, as provided FHR series were almost all confined within FHR-CTG +/- 3 bpm, where FHR-CTG were FHR series provided by commercial US cardiotocographic devices, currently employed in clinical routine.

Congenital nystagmus is an ocular-motor disorder that develops in the first few months of life; its pathogenesis is still unknown. Patients affected by congenital nystagmus show continuous, involuntary, rhythmical oscillations of the eyes. Monitoring eye movements, nystagmus main features such as shape, amplitude and frequency, can be extracted and analysed. Previous studies highlighted, in some cases, a much slower and smaller oscillation, which appears added up to the ordinary nystagmus waveform. This sort of baseline oscillation, or slow nystagmus, hinder precise cycle-to-cycle image placement onto the fovea. Such variability of the position may reduce patient visual acuity. This study aims to analyse more extensively eye movements recording including the baseline oscillation and investigate possible relationships between these slow oscillations and nystagmus. Almost 100 eye movement recordings (either infrared-oculographic or electrooculographic), relative to different gaze positions, belonging to 32 congenital nystagmus patients were analysed. The baseline oscillation was assumed sinusoidal; its amplitude and frequency were computed and compared with those of the nystagmus by means of a linear regression analysis. The results showed that baseline oscillations were characterised by an average frequency of 0.36 Hz (SD 0.11 Hz) and an average amplitude of 2.1° (SD 1.6°). It also resulted in a considerable correlation (R2 scored 0.78) between nystagmus amplitude and baseline oscillation amplitude; the latter, on average, resulted to be about one-half of the correspondent nystagmus amplitude.

Congenital nystagmus (CN) is an ocular-motor disorder characterised by involuntary, conjugated ocular oscillations and its pathogenesis is still under investigation. This kind of nystagmus is termed congenital (or infantile) since it could be present at birth or it can arise in the first months of life. Most of CN patients show a considerable decrease of their visual acuity: image fixation on the retina is disturbed by nystagmus continuous oscillations, mainly horizontal. However, the image of a given target can still be stable during short periods in which eye velocity slows down while the target image is placed onto the fovea (called foveation intervals). To quantify the extent of nystagmus, eye movement recording are routinely employed, allowing physicians to extract and analyse nystagmus main features such as waveform shape, amplitude and frequency. Using eye movement recording, it is also possible to compute estimated visual acuity predictors: analytical functions which estimates expected visual acuity using signal features such as foveation time and foveation position variability. Use of those functions extend the information from typical visual acuity measurement (e.g. Landolt C test) and could be a support for therapy planning or monitoring. This study focuses on detection of CN patients' waveform type and on foveation time measure. Specifically, it proposes a robust method to recognize cycles corresponding to the specific CN waveform in the eye movement pattern and, for those cycles, evaluate the exact signal tracts in which a subject foveates. About 40 eyemovement recordings, either infrared-oculographic or electrooculographic, were acquired from 16 CN subjects. Results suggest that the use of an adaptive threshold applied to the eye velocity signal could improve the estimation of slow phase start point. This can enhance foveation time computing and reduce influence of repositioning saccades and data noise on the waveform type identification.

Congenital nystagmus (CN) is an ocular-motor disorder characterised by involuntary, conjugated ocular oscillations, that can arise since the first months of life. Pathogenesis of congenital nystagmus is still under investigation. In general, CN patients show a considerable decrease of their visual acuity: image fixation on the retina is disturbed by nystagmus continuous oscillations, mainly horizontal. However, image stabilisation is still achieved during the short periods in which eye velocity slows down while the target image is placed onto the fovea (called foveation intervals). To quantify the extent of nystagmus, eye movement recording are routinely employed, allowing physicians to extract and analyse nystagmus main features such as shape, amplitude and frequency. Using eye movement recording, it is also possible to compute estimated visual acuity predictors: analytical functions which estimates expected visual acuity using signal features such as foveation time and foveation position variability. Use of those functions add information to typical visual acuity measurement (e.g. Landolt C test) and could be a support for therapy planning or monitoring. This study focus on robust detection of CN patients' foveations. Specifically, it proposes a method to recognize the exact signal tracts in which a subject foveates, This paper also analyses foveation sequences. About 50 eyemovement recordings, either infrared-oculographic or electrooculographic, from different CN subjects were acquired. Results suggest that an exponential interpolation for the slow phases of nystagmus could improve foveation time computing and reduce influence of breaking saccades and data noise. Moreover a concise description of foveation sequence variability can be achieved using non-fitting splines. © 2009 Springer Berlin Heidelberg.

Congenital nystagmus (CN) is an ocular-motor disorder that appears at birth or during the first few months of life; it is characterised by involuntary, conjugated, bilateral to and fro ocular oscillations. Pathogenesis of congenital nystagmus is still unknown. Eye movement recording allow to extract and analyse nystagmus main features such as shape, amplitude and frequency; depending on the morphology of the oscillations nystagmus can be classified in different categories (pendular, jerk, horizontal unidirectional, bidirectional). In general, CN patient show a considerable decrease of the visual acuity: image fixation on the retina is disturbed by nystagmus continuous oscillations; however, image stabilisation is still achieved during the short foveation periods in which eye velocity slows down while the target image is placed onto the fovea. Visual acuity was found to be mainly dependent on foveation periods duration, but cycle-to-cycle foveation repeatability and reduction of retinal image velocities also contribute in increasing visual acuity. This study concentrate on cycle-to-cycle image position variation onto fovea, trying to characterise the sequences of foveation positions. Eye-movement (infrared oculographic or electro oculographic) recordings, relative to different gaze positions and belonging to more than 30 CN patients, were analysed. Preliminary results suggest that sequences of foveations show a cyclic pattern with a dominant frequency (around 0.3 Hz on average) much lower than that of the nystagmus (about 3.3 Hz on average). Sequences of foveations reveals an horizontal ocular swing of more than 2 degree on average, which can explain the low visual acuity of the CN patient. Current CN therapies, pharmacological treatment or surgery of the ocular muscles, mainly aim to increase the patient's visual acuity. Hence, it is fundamental to have an objective parameter (expected visual acuity) for therapy planning. The information about sequences of foveations can improve estimation of patient visual acuity.

— Long term recording of biomedical signals such as ECG, EMG, respiration and other information (e.g. body motion) can improve diagnosis and potentially monitor the evolution of many widespread diseases. However, long term monitoring requires specific solutions, portable and wearable equipment that should be particularly comfortable for patients. The key-issues of portable biomedical instrumentation are: power consumption, long-term sensor stability, comfortable wearing and wireless connectivity. In this scenario, it would be valuable to realize prototypes using available technologies to assess long-term personal monitoring and foster new ways to provide healthcare services. The aim of this work is to discuss the advantages and the drawbacks in long term monitoring of biopotentials and body movements using textile electrodes embedded in clothes. The textile electrodes were embedded into garments; tiny shirt and short were used to acquire electrocardiographic and electromyographic signals. The garment was equipped with low power electronics for signal acquisition and data wireless transmission via Bluetooth. A small, battery powered, biopotential amplifier and three-axes acceleration body monitor was realized. Patient monitor incorporates a microcontroller, analog-to-digital signal conversion at programmable sampling frequencies. The system was able to acquire and to transmit realtime signals, within 10 m range, to any Bluetooth device (including PDA or cellular phone). The electronics were embedded in the shirt resulting comfortable to wear for patients. Small size MEMS 3-axes accelerometers were also integrated.

Auscultation and observation of foetal heart rate (FHR), through various techniques, has always been of crucial importance to ascertain life and well-being of the foetus. Gynaecologists evaluate several characteristics of FHR signal, but foetal heart rate variability (FHRV) is probably the most important feature of the FHR recording, due to its relationship to the autonomic and central nervous systems, as demonstrated by pharmacological experiments in both foetal sheep and adult humans [1]. However, there is a high inter and intra-observer variability [2] in the interpretation of FHR signals and there are no clear standards for the definition and evaluation of FHR changes. This is particularly true when assessing beat-to-beat variability of the heart rate [1, 3, 4]. In frequency domain, as well as adults, most of literature [1, 5, 6] agrees that three bands can be detected in the FHR power spectrum, a very low frequency band (VLF), a low frequency band (LF) and a high frequency band (HF). However, despite to the spread use of FHRV spectral analysis [7, 8, 9, 10], there is not agreement in definition of its components. Some researcher groups, for example, identify the VLF with the range [0 ??? 0.03] Hz [6], while other authors consider as VLF the range [0 ??? 0.04] Hz [11] or [0 ??? 0.05]Hz [1]. Moreover, some authors introduce also a medium frequency band (MF), corresponding to maternal respiration [12]. Besides, differently from what happens in the adult, there are modifications of the baseline, accelerations and decelerations, which make more difficult or even impossible FHRV definition and identification. Some guidelines in fact state that variability refers to fluctuations in the baseline free from accelerations and decelerations [13]. This is an important limit in clinical routine since variability in correspondence of these FHR alterations has always been regarded as particularly significant in terms of prognostic value [14, 15]. Aim of this work was to propose the computation of FHRV signal as difference between FHR and floatingline, which is an imaginary median line of FHR signal [16, 17] and that hence includes accelerations and decelerations. Further, we characterised floatingline in frequency domain, and, to this purpose, a characterisation of accelerations and decelerations has been necessary, being the literature very poor at this regard. Finally, a procedure to estimate floatingline in time domain was proposed.

A classification system able to evaluate the performances of a transition radiation detector prototype for electrons/hadrons discrimination is presented. It is based both on a layered feed-forward neural network trained using back-propagation and a likehood ratio technique. The information fed into the classification system consists of the number of hits detected by each multiwires proportional chamber of the detector. The best results are obtained by the neural network approach that successfully identifies 4.0 GeV/ c electrons with an hadron contamination of about 4x10 -3 at 98% acceptance efficiency.

This study extends a previous research concerning intervertebral motion registration by means of 2D dynamic fluoroscopy to obtain a more comprehensive 3D description of vertebral kinematics. The problem of estimating the 3D rigid pose of a CT volume of a vertebra from its 2D X-ray fluoroscopy projection is addressed. 2D-3D registration is obtained maximising a measure of similarity between Digitally Reconstructed Radiographs (obtained from the CT volume) and real fluoroscopic projection. X-ray energy correction was performed. To assess the method a calibration model was realised a sheep dry vertebra was rigidly fixed to a frame of reference including metallic markers. Accurate measurement of 3D orientation was obtained via single-camera calibration of the markers and held as true 3D vertebra position; then, vertebra 3D pose was estimated and results compared. Error analysis revealed accuracy of the order of 0.1 degree for the rotation angles of about 1 mm for displacements parallel to the fluoroscopic plane, and of order of 10 mm for the orthogonal displacement.

Analysis of dynamic videofluoroscopic can provide spine kinematic data with an acceptable low X-ray dose. Estimation of the kinematics relies on accurate recognition of vertebrae positions and rotations on each radiological frame. In previous works we presented a procedure for automatic tracking of vertebra motion by smoothed gradient operators and template matching in fluoroscopic image sequences. A limitation to the accurate estimation of the kinematics by automatic tracking of vertebrae motion, independently by the specific methodology employed (e.g. manual marking, corner or edge automatic detection, etc.), is mainly due to noise: low-dose X-ray image sequences exhibit severe signal-dependent noise that should be reduced, while preserving anatomical edges and structures. Noise in low-dose X-ray images originates from various sources, however quantum noise is by far the more dominant noise in low-dose X-ray images and other sources can be neglected. Signal degraded by quantum noise is commonly modeled by a Poisson distribution, but it is possible to approximate it as additive zero-mean Gaussian noise with signal-dependent variance. In this work we propose a digital spatial filter for reducing noise in low-dose X-ray images. The proposed filter is based on averaging of only similar pixels (whose grey level is contained within ±3σ) instead of spatial averaging of all neighbouring pixels. The effectiveness of the filter performance was evaluated by fluoroscopic image sequence processing, comparing the results of the automatic vertebra tracking on filtered and unfiltered images.

Monitoring foetal health is important to appropriately plan pregnancy management and delivery. Cardiotocography (CTG) is one of the most employed diagnostic techniques. Because CTG interpretation still lacks of complete reliability, new methods of interpretation and parameters are necessary to further support physicians’ decisions. To this aim, indexes related to variability of foetal heart rate (FHRV) are particularly studied. Frequency components of FHRV and their modifications can be analysed by applying a time-frequency approach, which allows for a distinct understanding of the spectral components related to foetal reactions to internal and external stimuli and their change over time. Being uterine contractions (UC) strong stimuli for the foetus and his autonomic nervous system (ANS), it is worth exploring the FHRV response to UC. This study analysed modifications of FHRV frequency characteristics with respect to 108 UC (relative to 35 healthy foetuses). Results showed a statistically significant (t-test, p < 0.01) power increase of the FHRV in both LF and HF bands in correspondence of the contractions. Moreover, we observed a shift to higher values of the maximum frequency contained in the signal corresponding to the power increase. Such modifications of the FHRV power spectrum can be a sign of ANS reaction and therefore represent additional, objective information about foetal reactivity and health during labour.

We investigate the retrieval properties of fully connected saturated neural networks trained by the Edinburgh algorithm with correlated patterns {ξi}. We evaluate the effect of the correlation on the overlap between {ξi} and the network configuration after one time step and on the basins of attraction. We also show that the introduction of the thermal noise does not change the domains of attraction of the correlated memories.

The expected high volume imagery data from Nasa Mission to the Planet Earth is one of the target application areas for automated cloud retrieval, and more generally for automated image classification. We used the backpropagation (BP), the cascade-correlation (CC) and Kohonen self-organizing map (SOM) neural network architectures for cloud retrieval from satellite imagery. We have used a simple scene (a mixed scene containing only cloud and ocean). This simple scene allows us to evaluate the accuracy of the classification better than a complicated scene. Both BP and CC performed at the same accuracy level, while the SOM algorithm was slightly less accurate in performing unsupervised learning. This study shows that for simple scenes, which are abundant in global monitoring satellite imagery, a simple pixel-by-pixel or 3x3 window approaches provide high accuracy classification without using complicated contextual information.

A neural network based system for the localization and tracking of a moving target in space related scenes is described. The neural control system is used to "drive" the input sensor on the image plane by means of sequential moves, in order to overlap the final position of the moving sensor to the target.

Problems related to rapid transformation of land-use have reached a significant impact level. Photo interpretation by human experts has been the major data source for disasters' monitoring and urban planning applications. However, images collected from a sensor which combines reasonably good spectral and spatial resolution images acquired by IKONOS II satellite and a RGB-HIS transformation technique have been considered to merge spectral and spatial information. Then, textural information, which characterizes urban area, has been extracted with the use of an appropriate filter for edge extraction. An MLP classifier has been trained to produce a labeled image with great accuracy in test even if a limited training set has been used. For a photo interpreter, the results reveal a good feasibility of the classified image for monitoring the presence of changes in urban areas, useful for cartographic updating.

The authors present two artificial neural network architectures that perform offline segmentation of telerobotics tasks using force data. Two network architectures have been tested. The first one is based on turning temporal sequences into spatial patterns. The second architecture extends the first model by including the network's output in the input array. Experimental data are classified offline by a hidden Markov model providing the transition times and the corresponding segmentation for the training data. It was found that the first architecture needs a high number of iterations for learning the associations, whereas the latter has a high convergence speed. >

Discussion: Each sensor on the pressure mat is 0.7 cm 2 in area. Since the error for the COP coordinates are less than the size of the sensor, the discrepancies between the two software packages can be considered negligible. Regarding the COP accuracy, the resulting errors are less than 4% of the subject’s applied pressure and can therefore be considered minor. Further studies are required to verify the repeatability of these results. Following will be a full integration with an ad hoc SR already in use in the laboratory. References

This paper describes a research activity devoted to verify the feasibility of a neural network approach for automatic detection of naval targets in radar imagery. The activity is part of a more ambitious industrial project concerning the use of advanced image processing techniques for improving the safety in maritime surveillance of harbour traffic. Having in mind this final application, the task of automatic target detection has to fulfil two main requirements: 1) quasi real time response, in the sense that each input radar image of about 1000 by 1000 pixels has to be analyzed in a time comparable with the sweep period of the acquisition system, i.e. three seconds; 2)very high accuracy, i.e. each ship in the scene must be surely detected: this means that the efficiency of the detection system should be not minus than 100%. These two applicative needs have to meet with the high degree of noise (clutter) characterizing the input radar data. In order to face up to the complexity of the exposed goal, the task has been decomposed in a set of sequential subtasks: noise elimination, preliminary object recognition and classified image reconstruction. Moreover, both neural and traditional tools have been used, designing a multi-modular hybrid system (a detailed discussion about the advantages of hybrid architecture can be found, for example, in (F. Folgelman Suolie, 1993)).

This paper describes research within the CNR’s Progetto Finalizzato ‘Tecnologie Biomediche e Sanitarie’, subproject ‘ultrasounds’. The aims of this study were as follows: (1) in the field of digital processing of B-scan images, aimed at tissue characterization by means of texture analysis; and (2) in the design and implementation of an echographic unit for ophthalmology, with advanced technical features. The prototype of the electronic interface between a 64-element linear curtain transducer, at 10 MHz, and the videofrequency section of the last unit is working actually, included in a commercial echograph produced by ESAOTE-Biomedica. In this paper, experimental results obtained with the developed hardware are shown. The integration of hardware and software modules within a single advanced unit is currently in progress. It will be based on a personal computer, supported by a video interface and by the hardware modules now under test.

The blood radioactivity curve after i.v. injection of 1 microC/kg of C14 labeled serotonin runs significantly lower in schizophrenic than in normal subjects.

Whole Body Vibration training is more and more utilized in sport medicine to enhance athletic performance and training; recently, vibration treatment is being also used for therapy and rehabilitation for patients affected by different pathologies. It is also worth mention that excessive vibrations can be hazardous. The treatment is based on the hypothesis that under some circumstances vibration loads induce specific responses from the neuromuscular system; some clinical evidences suggest mechanical and metabolic reaction. Usually, vibrations are transmitted by means of a platform oscillating at different frequencies (10-80Hz) to patient body; mainly, limb muscles are involved. Many studies about the subject employ surface EMG recordings to evaluate muscle activity during vibration training. On electrodes, this condition generates large motion artifact at the vibration frequency. To get rid of such artifact an adaptive filter was designed: accelerometers placed onto platform or directly on muscles provide an error signal shape to be cancelled from the raw EMG. In particular, surface EMG have been recorded from leg quadriceps muscles of volunteers during whole body vibration therapy sessions at different oscillating frequency. A standard RLS adaptive filter have been used to cancel the motion artifact from EMG in real-time. Results show the effective cancellations of the vibration frequency from the raw EMG; the RMS value of the cleaned EMG results lower than that unprocessed in some cases up to 50%.

Beside other Clinical Engineering activities to reduce risks in hospitals, functional assessment of medical devices is becoming more and more extended. Once evaluated the common risk such those electric and mechanical, specific risk are connected to the device performance: functional assessment aims to verify the specific proper operations of a medical apparatus. In particular, neonatal incubators have the primary aim to maintain an adequate microclimate for the newborn, especially those premature and smaller, who may not be able to regulates body temperature. To prevent newborn heat loss, incubator mainly stabilizes the temperature of the baby compartment to a selected value, reducing the risk of hypothermia. As suggested by the Particular Standards for safety of baby incubators, temperature have to be measured in 5 specific points inside infant chamber: the difference between measured temperatures and that selected on incubator must be limited to fractions of °C. Also the time needed to reach a given temperature must be limited. Other tests should be performed to verify humidity, air velocity, noise level, CO2 concentration, etc.. A prototype device was developed to automatically perform functional assessment of neonatal incubators. A microcontroller continuously performs a 12-bit analog to digital conversion of voltage signals generated by 5 thermal probes and a humidity sensor and sends those data to a PC, where a software process received signal and provide tests. The HW device is of little dimensions, battery powered and capable to continuously transmit data wirelessly, the SW allows real-time signal display, average values, optic and acoustic alarms for limits exceeding, data storage, etc. The device can be easily used to perform periodical functional tests on neonatal incubators. Further analysis of recorded data can be used to evaluate more specific features such as modalities of temperature control, etc.

The authors report their continuing research aimed at computer aided echography by processing B-scan videofrequency images with SGLD method. They find a reduction of subjectivity of diagnosis of pathology.

Many works in the field of image processing stress the utility of using Artificial Intelligence tools to obtain enhanced performances in the scene understanding task. Feigenbaum (1977) states that “..the power of an expert system derives from the knowledge it possesses not from the particular formalism and inference schemas it employs…”. In our opinion this assertion can be extended to all complex cognitive problems (such as natural language and automatic image understanding) in which human reasoning capabilities are required. “From this point of view a theory for the vision problem resolution must necessarily contain elements of a more general theory of thinking (Minsky, 1974)”. In order to translate these philosophical statements into action, we have to understand what kind of knowledge is useful for the vision problem resolution and how to represent it. Looking at the image understanding problem as a perception problem it is possible to individuate two different knowledge sources. The first one descends from the perceptual grouping laws of visible entities; the other derives directly from an explicit description of the objects to be recognized in the image describing a real scene. Normally these two knowledges are used in a hierarchical fashion with a special emphasis on one source at the expenses of the other. Moreover in many of these applications a low level processing attached to the image extracts features and a high level step tries to match the previously selected features with the model descriptions. A drawback of this approach is that an intelligent matching can be applied to an essentially non intelligent image partition.

SummaryA multi-centre double-blind crossover study was carried out in 70 hospitalised patients with rheumatoid arthritis to compare the activity of diftalone, a new anti-inflammatory agent, with that of indomethacin.Patients received either 750 mg. diftalone or 100 mg. indomethacin daily for 2 weeks and were then crossed over to the alternative treatment for a further 2 weeks. Routine clinical and laboratory assessments of disease activity were carried out before the start of the trial and after each treatment period. The results indicate that both drugs produced a favourable response and that there was no statistically significant difference between them in the parameters measured, except for the erythrocyte sedimentation rate which fell only after the administration of diftalone. Patient and physician assessments on the course of the disease were in general agreement and were favourable to the same extent for both treatments. Both products were well tolerated and there were few reports of side-effects.

Congenital nystagmus (CN) is a disturbance of the oculomotor centers which develops at birth or in the first months of life. Nystagmus consists essentially in involuntary, conjugated, horizontal rhythmic movements of the eye. Its pathogenesis is still unknown. Current therapies for CN aim to increase the patient’s visual acuity by means of refraction defects correction, drug delivery and ocular muscle surgery. Eye movement recording supports for accurate diagnosis, for patient follow-up and for therapy evaluation. In general, CN patients show a considerable decrease of visual acuity (image fixation on the retina is obstructed by nystagmus continuous oscillations) and severe postural alterations such as the anomalous head position, searched by patient to obtain a better fixation of the target image onto the retina. Often CN presents ‘neutral zones’ corresponding to particular gaze angles, in which nystagmus amplitude minimizes allowing a longer foveation time and a more stable repositioning of foveations, increasing visual acuity. Selected patients’ eye movements were recorded by using EOG or infrared oculography devices. Visual stimulation was delivered by means of an arched LED bar covering a visual field of –30 +30 degrees with respect to the central position. Computation of CN concise parameters allows in-dept analysis of foveations and estimation of visual acuity at different gaze angles. Preliminary results show a maximum of visual acuity at a specific gaze angle; this angle is mostly located at the patient’s right side for the analyzed group.